Overview

Brought to you by YData

Dataset statistics

Number of variables12
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.5 KiB
Average record size in memory104.0 B

Variable types

Numeric12

Alerts

avg_basket_size is highly overall correlated with gross_revenue and 1 other fieldsHigh correlation
avg_recency_days is highly overall correlated with frequencyHigh correlation
avg_ticket is highly overall correlated with avg_unique_basket_sizeHigh correlation
avg_unique_basket_size is highly overall correlated with avg_ticket and 1 other fieldsHigh correlation
frequency is highly overall correlated with avg_recency_daysHigh correlation
gross_revenue is highly overall correlated with avg_basket_size and 3 other fieldsHigh correlation
qtde_invoices is highly overall correlated with gross_revenue and 3 other fieldsHigh correlation
qtde_items is highly overall correlated with avg_basket_size and 3 other fieldsHigh correlation
qtde_products is highly overall correlated with avg_unique_basket_size and 3 other fieldsHigh correlation
recency_days is highly overall correlated with qtde_invoicesHigh correlation
avg_ticket is highly skewed (γ1 = 53.44422362) Skewed
frequency is highly skewed (γ1 = 24.88049136) Skewed
qtde_returns is highly skewed (γ1 = 51.79774426) Skewed
avg_basket_size is highly skewed (γ1 = 44.67271661) Skewed
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2025-07-07 03:02:43.491175
Analysis finished2025-07-07 03:02:56.920072
Duration13.43 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

customer_id
Real number (ℝ)

Unique 

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.773
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:02:57.016811image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.9903
Coefficient of variation (CV)0.11256734
Kurtosis-1.2060947
Mean15270.773
Median Absolute Deviation (MAD)1488
Skewness0.031607859
Sum45338925
Variance2954927.6
MonotonicityNot monotonic
2025-07-07T00:02:57.248240image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12558 1
 
< 0.1%
17850 1
 
< 0.1%
13047 1
 
< 0.1%
12583 1
 
< 0.1%
13748 1
 
< 0.1%
15100 1
 
< 0.1%
15291 1
 
< 0.1%
14688 1
 
< 0.1%
17809 1
 
< 0.1%
16956 1
 
< 0.1%
Other values (2959) 2959
99.7%
ValueCountFrequency (%)
12347 1
< 0.1%
12348 1
< 0.1%
12352 1
< 0.1%
12356 1
< 0.1%
12358 1
< 0.1%
12359 1
< 0.1%
12360 1
< 0.1%
12362 1
< 0.1%
12364 1
< 0.1%
12370 1
< 0.1%
ValueCountFrequency (%)
18287 1
< 0.1%
18283 1
< 0.1%
18282 1
< 0.1%
18277 1
< 0.1%
18276 1
< 0.1%
18274 1
< 0.1%
18273 1
< 0.1%
18272 1
< 0.1%
18270 1
< 0.1%
18269 1
< 0.1%

gross_revenue
Real number (ℝ)

High correlation 

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.3217
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:02:57.489535image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.623
Coefficient of variation (CV)3.8484486
Kurtosis353.94472
Mean2749.3217
Median Absolute Deviation (MAD)672.16
Skewness16.777556
Sum8162736.2
Variance1.1194959 × 108
MonotonicityNot monotonic
2025-07-07T00:02:57.706258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
533.33 2
 
0.1%
734.94 2
 
0.1%
178.96 2
 
0.1%
1078.96 2
 
0.1%
598.2 2
 
0.1%
1314.45 2
 
0.1%
379.65 2
 
0.1%
2053.02 2
 
0.1%
331 2
 
0.1%
889.93 2
 
0.1%
Other values (2944) 2949
99.3%
ValueCountFrequency (%)
6.2 1
< 0.1%
13.3 1
< 0.1%
15 1
< 0.1%
36.56 1
< 0.1%
45 1
< 0.1%
52 1
< 0.1%
52.2 1
< 0.1%
52.2 1
< 0.1%
62.43 1
< 0.1%
68.84 1
< 0.1%
ValueCountFrequency (%)
279138.02 1
< 0.1%
259657.3 1
< 0.1%
194550.79 1
< 0.1%
168472.5 1
< 0.1%
140450.72 1
< 0.1%
124564.53 1
< 0.1%
117379.63 1
< 0.1%
91062.38 1
< 0.1%
72882.09 1
< 0.1%
66653.56 1
< 0.1%

recency_days
Real number (ℝ)

High correlation  Zeros 

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.287639
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:02:57.945605image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.756779
Coefficient of variation (CV)1.2095137
Kurtosis2.7779627
Mean64.287639
Median Absolute Deviation (MAD)26
Skewness1.7983795
Sum190870
Variance6046.1167
MonotonicityNot monotonic
2025-07-07T00:02:58.176067image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 99
 
3.3%
4 87
 
2.9%
3 85
 
2.9%
2 85
 
2.9%
8 76
 
2.6%
10 67
 
2.3%
9 66
 
2.2%
7 66
 
2.2%
17 64
 
2.2%
16 55
 
1.9%
Other values (262) 2219
74.7%
ValueCountFrequency (%)
0 34
 
1.1%
1 99
3.3%
2 85
2.9%
3 85
2.9%
4 87
2.9%
5 43
1.4%
7 66
2.2%
8 76
2.6%
9 66
2.2%
10 67
2.3%
ValueCountFrequency (%)
373 2
0.1%
372 4
0.1%
371 1
 
< 0.1%
368 1
 
< 0.1%
366 4
0.1%
365 2
0.1%
364 1
 
< 0.1%
360 1
 
< 0.1%
359 1
 
< 0.1%
358 4
0.1%

qtde_invoices
Real number (ℝ)

High correlation 

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7231391
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:02:58.393363image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.8565313
Coefficient of variation (CV)1.5474954
Kurtosis190.83445
Mean5.7231391
Median Absolute Deviation (MAD)2
Skewness10.766805
Sum16992
Variance78.438147
MonotonicityNot monotonic
2025-07-07T00:02:58.618942image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
1 190
 
6.4%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
Other values (46) 332
11.2%
ValueCountFrequency (%)
1 190
 
6.4%
2 785
26.4%
3 499
16.8%
4 393
13.2%
5 237
 
8.0%
6 173
 
5.8%
7 138
 
4.6%
8 98
 
3.3%
9 69
 
2.3%
10 55
 
1.9%
ValueCountFrequency (%)
206 1
< 0.1%
199 1
< 0.1%
124 1
< 0.1%
97 1
< 0.1%
91 2
0.1%
86 1
< 0.1%
72 1
< 0.1%
62 2
0.1%
60 1
< 0.1%
57 1
< 0.1%

qtde_items
Real number (ℝ)

High correlation 

Distinct1671
Distinct (%)56.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1608.8525
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:02:58.836371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile102.4
Q1296
median641
Q31401
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1105

Descriptive statistics

Standard deviation5887.578
Coefficient of variation (CV)3.6594891
Kurtosis465.99808
Mean1608.8525
Median Absolute Deviation (MAD)422
Skewness17.858591
Sum4776683
Variance34663575
MonotonicityNot monotonic
2025-07-07T00:02:59.342572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
310 11
 
0.4%
150 9
 
0.3%
88 9
 
0.3%
84 8
 
0.3%
260 8
 
0.3%
288 8
 
0.3%
272 8
 
0.3%
246 8
 
0.3%
114 7
 
0.2%
134 7
 
0.2%
Other values (1661) 2886
97.2%
ValueCountFrequency (%)
1 1
< 0.1%
2 2
0.1%
12 2
0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
23 1
< 0.1%
25 1
< 0.1%
ValueCountFrequency (%)
196844 1
< 0.1%
80997 1
< 0.1%
80263 1
< 0.1%
77373 1
< 0.1%
69993 1
< 0.1%
64549 1
< 0.1%
64124 1
< 0.1%
63312 1
< 0.1%
58343 1
< 0.1%
57885 1
< 0.1%

qtde_products
Real number (ℝ)

High correlation 

Distinct468
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.72415
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:02:59.611697image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.89641
Coefficient of variation (CV)2.1992119
Kurtosis354.86113
Mean122.72415
Median Absolute Deviation (MAD)44
Skewness15.707635
Sum364368
Variance72844.071
MonotonicityNot monotonic
2025-07-07T00:02:59.840405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 43
 
1.4%
20 37
 
1.2%
29 35
 
1.2%
35 35
 
1.2%
19 34
 
1.1%
15 33
 
1.1%
11 32
 
1.1%
26 31
 
1.0%
27 30
 
1.0%
25 30
 
1.0%
Other values (458) 2629
88.5%
ValueCountFrequency (%)
1 6
 
0.2%
2 14
0.5%
3 16
0.5%
4 17
0.6%
5 26
0.9%
6 29
1.0%
7 18
0.6%
8 19
0.6%
9 26
0.9%
10 28
0.9%
ValueCountFrequency (%)
7838 1
< 0.1%
5673 1
< 0.1%
5095 1
< 0.1%
4580 1
< 0.1%
2698 1
< 0.1%
2379 1
< 0.1%
2060 1
< 0.1%
1818 1
< 0.1%
1673 1
< 0.1%
1637 1
< 0.1%

avg_ticket
Real number (ℝ)

High correlation  Skewed 

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.897762
Minimum2.1505882
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:03:00.020538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum2.1505882
5-th percentile4.9166611
Q113.119333
median17.956587
Q324.988286
95-th percentile90.497
Maximum56157.5
Range56155.349
Interquartile range (IQR)11.868952

Descriptive statistics

Standard deviation1036.9344
Coefficient of variation (CV)19.98033
Kurtosis2890.7071
Mean51.897762
Median Absolute Deviation (MAD)5.984842
Skewness53.444224
Sum154084.45
Variance1075233
MonotonicityNot monotonic
2025-07-07T00:03:00.224481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.47833333 2
 
0.1%
15 2
 
0.1%
4.162 2
 
0.1%
33.86607143 1
 
< 0.1%
292 1
 
< 0.1%
45.32647059 1
 
< 0.1%
6.932857143 1
 
< 0.1%
5.66585034 1
 
< 0.1%
18.15222222 1
 
< 0.1%
80.13043478 1
 
< 0.1%
Other values (2956) 2956
99.6%
ValueCountFrequency (%)
2.150588235 1
< 0.1%
2.4325 1
< 0.1%
2.462371134 1
< 0.1%
2.511241379 1
< 0.1%
2.515333333 1
< 0.1%
2.65 1
< 0.1%
2.656931818 1
< 0.1%
2.707598253 1
< 0.1%
2.760621572 1
< 0.1%
2.770464191 1
< 0.1%
ValueCountFrequency (%)
56157.5 1
< 0.1%
4453.43 1
< 0.1%
3202.92 1
< 0.1%
1687.2 1
< 0.1%
952.9875 1
< 0.1%
872.13 1
< 0.1%
841.0214493 1
< 0.1%
651.1683333 1
< 0.1%
640 1
< 0.1%
624.4 1
< 0.1%

avg_recency_days
Real number (ℝ)

High correlation 

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.348511
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:03:00.439538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.923077
median48.285714
Q385.333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.410256

Descriptive statistics

Standard deviation63.544929
Coefficient of variation (CV)0.94352388
Kurtosis4.8871091
Mean67.348511
Median Absolute Deviation (MAD)26.285714
Skewness2.0627709
Sum199957.73
Variance4037.958
MonotonicityNot monotonic
2025-07-07T00:03:00.640751image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 25
 
0.8%
4 22
 
0.7%
70 21
 
0.7%
7 20
 
0.7%
35 19
 
0.6%
49 18
 
0.6%
21 17
 
0.6%
46 17
 
0.6%
11 17
 
0.6%
42 16
 
0.5%
Other values (1248) 2777
93.5%
ValueCountFrequency (%)
1 16
0.5%
1.5 1
 
< 0.1%
2 13
0.4%
2.5 1
 
< 0.1%
2.601398601 1
 
< 0.1%
3 15
0.5%
3.321428571 1
 
< 0.1%
3.330357143 1
 
< 0.1%
3.5 2
 
0.1%
4 22
0.7%
ValueCountFrequency (%)
366 1
 
< 0.1%
365 1
 
< 0.1%
363 1
 
< 0.1%
362 1
 
< 0.1%
357 2
0.1%
356 1
 
< 0.1%
355 2
0.1%
352 1
 
< 0.1%
351 2
0.1%
350 3
0.1%

frequency
Real number (ℝ)

High correlation  Skewed 

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137973
Minimum0.0054495913
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:03:00.879566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.0054495913
5-th percentile0.0088941642
Q10.016339869
median0.025889968
Q30.049450549
95-th percentile1
Maximum17
Range16.99455
Interquartile range (IQR)0.03311068

Descriptive statistics

Standard deviation0.40815625
Coefficient of variation (CV)3.5866953
Kurtosis989.36508
Mean0.1137973
Median Absolute Deviation (MAD)0.012191338
Skewness24.880491
Sum337.8642
Variance0.16659153
MonotonicityNot monotonic
2025-07-07T00:03:01.117533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 198
 
6.7%
0.0625 18
 
0.6%
0.02777777778 17
 
0.6%
0.02380952381 16
 
0.5%
0.08333333333 15
 
0.5%
0.09090909091 15
 
0.5%
0.02941176471 14
 
0.5%
0.03448275862 14
 
0.5%
0.02564102564 13
 
0.4%
0.01923076923 13
 
0.4%
Other values (1215) 2636
88.8%
ValueCountFrequency (%)
0.005449591281 1
 
< 0.1%
0.005464480874 1
 
< 0.1%
0.005479452055 1
 
< 0.1%
0.005494505495 1
 
< 0.1%
0.005586592179 2
0.1%
0.005602240896 1
 
< 0.1%
0.005617977528 2
0.1%
0.00566572238 1
 
< 0.1%
0.005681818182 2
0.1%
0.005698005698 3
0.1%
ValueCountFrequency (%)
17 1
 
< 0.1%
3 1
 
< 0.1%
2 6
 
0.2%
1.142857143 1
 
< 0.1%
1 198
6.7%
0.75 1
 
< 0.1%
0.6666666667 3
 
0.1%
0.550802139 1
 
< 0.1%
0.5335120643 1
 
< 0.1%
0.5 3
 
0.1%

qtde_returns
Real number (ℝ)

Skewed  Zeros 

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.156955
Minimum0
Maximum80995
Zeros1481
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:03:01.329214image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q39
95-th percentile100.6
Maximum80995
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.4961
Coefficient of variation (CV)24.333498
Kurtosis2765.5286
Mean62.156955
Median Absolute Deviation (MAD)1
Skewness51.797744
Sum184544
Variance2287644.6
MonotonicityNot monotonic
2025-07-07T00:03:01.608873image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
6 78
 
2.6%
5 61
 
2.1%
12 51
 
1.7%
8 43
 
1.4%
7 43
 
1.4%
Other values (204) 706
23.8%
ValueCountFrequency (%)
0 1481
49.9%
1 164
 
5.5%
2 148
 
5.0%
3 105
 
3.5%
4 89
 
3.0%
5 61
 
2.1%
6 78
 
2.6%
7 43
 
1.4%
8 43
 
1.4%
9 41
 
1.4%
ValueCountFrequency (%)
80995 1
< 0.1%
9014 1
< 0.1%
8004 1
< 0.1%
4427 1
< 0.1%
3768 1
< 0.1%
3332 1
< 0.1%
2878 1
< 0.1%
2022 1
< 0.1%
2012 1
< 0.1%
1776 1
< 0.1%

avg_basket_size
Real number (ℝ)

High correlation  Skewed 

Distinct1979
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.81376
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:03:01.849316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172.33333
Q3281.69231
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.44231

Descriptive statistics

Standard deviation791.55519
Coefficient of variation (CV)3.1685812
Kurtosis2255.5382
Mean249.81376
Median Absolute Deviation (MAD)83.083333
Skewness44.672717
Sum741697.07
Variance626559.62
MonotonicityNot monotonic
2025-07-07T00:03:02.100951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11
 
0.4%
114 10
 
0.3%
73 9
 
0.3%
86 9
 
0.3%
82 9
 
0.3%
136 8
 
0.3%
75 8
 
0.3%
88 8
 
0.3%
60 8
 
0.3%
130 7
 
0.2%
Other values (1969) 2882
97.1%
ValueCountFrequency (%)
1 2
0.1%
2 1
< 0.1%
3.333333333 1
< 0.1%
5.333333333 1
< 0.1%
5.666666667 1
< 0.1%
6.142857143 1
< 0.1%
7.5 1
< 0.1%
9 1
< 0.1%
9.5 1
< 0.1%
11 1
< 0.1%
ValueCountFrequency (%)
40498.5 1
< 0.1%
6009.333333 1
< 0.1%
4282 1
< 0.1%
3906 1
< 0.1%
3868.65 1
< 0.1%
2880 1
< 0.1%
2801 1
< 0.1%
2733.944444 1
< 0.1%
2518.769231 1
< 0.1%
2160.333333 1
< 0.1%

avg_unique_basket_size
Real number (ℝ)

High correlation 

Distinct906
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.484591
Minimum0.2
Maximum259
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size46.4 KiB
2025-07-07T00:03:02.305664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile2
Q17.6666667
median13.6
Q322.142857
95-th percentile46
Maximum259
Range258.8
Interquartile range (IQR)14.47619

Descriptive statistics

Standard deviation15.460307
Coefficient of variation (CV)0.8842247
Kurtosis29.317441
Mean17.484591
Median Absolute Deviation (MAD)6.6
Skewness3.4358615
Sum51911.752
Variance239.02111
MonotonicityNot monotonic
2025-07-07T00:03:02.523814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 42
 
1.4%
9 41
 
1.4%
8 39
 
1.3%
16 39
 
1.3%
14 38
 
1.3%
17 38
 
1.3%
7 36
 
1.2%
11 36
 
1.2%
5 36
 
1.2%
15 35
 
1.2%
Other values (896) 2589
87.2%
ValueCountFrequency (%)
0.2 1
 
< 0.1%
0.25 3
 
0.1%
0.3333333333 6
0.2%
0.4 1
 
< 0.1%
0.4090909091 1
 
< 0.1%
0.5 12
0.4%
0.5454545455 1
 
< 0.1%
0.5555555556 1
 
< 0.1%
0.5714285714 1
 
< 0.1%
0.6176470588 1
 
< 0.1%
ValueCountFrequency (%)
259 1
< 0.1%
177 1
< 0.1%
148 1
< 0.1%
127 1
< 0.1%
105 1
< 0.1%
104 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
95.5 1
< 0.1%
94.33333333 1
< 0.1%

Interactions

2025-07-07T00:02:55.641091image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:43.772780image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:44.704500image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:45.656178image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:46.781426image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:47.693108image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:49.107760image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:50.057518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:51.140501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:52.072060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:53.288451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:54.452744image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:55.713892image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:43.889501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:44.777486image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:45.729625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:46.848483image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:47.799922image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:49.182224image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:50.129579image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:51.219073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:52.232321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:53.382062image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:54.767009image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:55.787970image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:43.960834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:44.848039image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:45.806174image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:46.916452image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:47.901718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:49.257093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:50.387262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:51.292847image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:53.482257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:54.846034image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:44.035532image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:51.375418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:45.345676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:48.689641image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:49.736794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:54.050324image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:55.320187image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:56.365204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:44.478194image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:45.424403image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:46.544064image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:47.430752image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:48.811748image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:52.951951image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:55.399925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:56.440825image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:44.553518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:49.897377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-07-07T00:02:53.079330image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:54.251200image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:55.481270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:56.515702image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:44.633357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:45.582192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:46.708116image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:47.586054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:49.030819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:49.982410image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:51.067233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:51.994400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:53.194257image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:54.361249image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-07-07T00:02:55.568925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-07-07T00:03:02.728769image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
avg_basket_sizeavg_recency_daysavg_ticketavg_unique_basket_sizecustomer_idfrequencygross_revenueqtde_invoicesqtde_itemsqtde_productsqtde_returnsrecency_days
avg_basket_size1.000-0.0770.1880.402-0.1230.0270.5740.1000.7290.3830.210-0.098
avg_recency_days-0.0771.000-0.1220.1300.019-0.881-0.247-0.259-0.227-0.166-0.3960.108
avg_ticket0.188-0.1221.000-0.618-0.1310.0900.2460.0590.167-0.3770.1900.048
avg_unique_basket_size0.4020.130-0.6181.000-0.016-0.1210.104-0.1810.1470.516-0.0540.015
customer_id-0.1230.019-0.131-0.0161.000-0.002-0.0760.026-0.0700.013-0.0630.001
frequency0.027-0.8810.090-0.121-0.0021.0000.0900.0790.0800.0360.2340.018
gross_revenue0.574-0.2470.2460.104-0.0760.0901.0000.7700.9250.7440.372-0.415
qtde_invoices0.100-0.2590.059-0.1810.0260.0790.7701.0000.7160.6900.294-0.502
qtde_items0.729-0.2270.1670.147-0.0700.0800.9250.7161.0000.7300.344-0.408
qtde_products0.383-0.166-0.3770.5160.0130.0360.7440.6900.7301.0000.242-0.435
qtde_returns0.210-0.3960.190-0.054-0.0630.2340.3720.2940.3440.2421.000-0.120
recency_days-0.0980.1080.0480.0150.0010.018-0.415-0.502-0.408-0.435-0.1201.000

Missing values

2025-07-07T00:02:56.635478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-07T00:02:56.801459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
0178505391.21372.034.01733.0297.018.15222235.50000017.00000040.050.9705880.617647
1130473232.5956.09.01390.0171.018.90403527.2500000.02830235.0154.44444411.666667
2125836705.382.015.05028.0232.028.90250023.1875000.04032350.0335.2000007.600000
313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000004.800000
415100876.00333.03.080.03.0292.0000008.6000000.07317122.026.6666670.333333
5152914623.3025.014.02102.0102.045.32647123.2000000.04011529.0150.1428574.357143
6146885630.877.021.03621.0327.017.21978618.3000000.057221399.0172.4285717.047619
7178095411.9116.012.02057.061.088.71983635.7000000.03352041.0171.4166673.833333
81531160767.900.091.038194.02379.025.5434644.1444440.243316474.0419.7142866.230769
9160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714294.857143
customer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
4269177271060.2515.01.0645.066.016.0643946.01.0000006.0645.00000066.000000
427717232421.522.02.0203.036.011.70888912.00.1538460.0101.50000015.000000
427817468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.500000
428113596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000066.500000
4286148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.000000
429012479473.2011.01.0382.030.015.7733334.01.00000034.0382.00000030.000000
430514126706.137.03.0508.015.047.0753333.00.75000050.0169.3333334.666667
4309135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333104.000000
431415060301.848.04.0262.0120.02.5153331.02.0000000.065.50000020.000000
431912558269.967.01.0196.011.024.5418186.01.000000196.0196.00000011.000000